Since graphs are used to represent the complex systems in various domains as diverse as social network [1]}, human knowledge network [2]}, e-commerce [3]} and cybersecurity [4]}, the analysis and mining for graph data have attracted a surge of research attention in recent years. However, the bulk of the existing researches focus on static graphs, yet the real-world graph data often evolves over time [5]}. Taking social networks as an example, there are always fresh persons who enroll in the community every month, and the relation between individuals is changing every day. To model and analyze graphs where nodes and edges change over time, mining dynamic graphs gains increasing popularity in the community of graph analysis.
